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| dc.contributor.author | Takahashi, Masakazu | |
| dc.contributor.author | Sugahara, Noriyuki | |
| dc.contributor.author | Shibata, Masashi | |
| dc.date.accessioned | 2021-07-05T11:26:50Z | |
| dc.date.available | 2021-07-05T11:26:50Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Takahashi, Masakazu, Sugahara, Noriyuki and Shibata, Masashi (2020). Towards detecting morning surge from sleep self-evaluations. In : International Research Conference on Smart Computing and Systems Engineering, 2020. Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, p.1. | en_US |
| dc.identifier.uri | http://repository.kln.ac.lk/handle/123456789/23065 | |
| dc.description.abstract | This paper aims to analyze the blood pressure transition during sleep. Morning surge is a sudden increase in blood pressure from awakening or before awakening. Morning surge is implicated in cardiovascular diseases, such as Stroke, Angina Pectoris, and Myocardial Infarction. Morning surge has been detected mainly by the ABPM (Ambulatory Blood Pressure Monitoring) method, which measures blood pressure for 24-hours. Since the ABPM method cannot distinguish awakening and sleep automatically, their alternative method is forcibly delimiting time or manually processing based on behaviour records. Therefore, it is necessary to capture the blood pressure change under clear sleep separation. This paper employs two sleep criteria for accurate blood pressure during sleep. | en_US |
| dc.publisher | Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka | en_US |
| dc.subject | Blood pressure, Clustering, Hypertension, Machine learning, Unsupervised learning | en_US |
| dc.title | Towards detecting morning surge from sleep self-evaluations | en_US |
